[{"data":1,"prerenderedAt":37},["ShallowReactive",2],{"post-document:\u002Fdeep_learning\u002Fcomputer_vision\u002F2026\u002F08\u002F06\u002Fobject-detection\u002F":3},{"id":4,"title":5,"body":6,"categories":17,"date":20,"description":21,"extension":22,"image":23,"key_concepts":23,"last_modified_at":23,"legacyPath":24,"meta":25,"navigation":27,"part":23,"path":28,"published":27,"robots":23,"seo":29,"series":23,"stem":30,"strengths":23,"summary":31,"tags":32,"tradeoffs":23,"__hash__":36},"posts\u002Fposts\u002FDeep_Learning\u002FComputer_Vision\u002F2026-08-06-object-detection.md","객체 탐지 모델과 핵심 개념",{"type":7,"value":8,"toc":13},"minimark",[9],[10,11,12],"p",{},"Object Detection\nObject Detection 기본 구조\nOne-stage와 Two-stage Detector\nR-CNN 계열 발전 과정\nYOLO 발전 과정\nAnchor-based와 Anchor-free\nNMS와 중복 박스 제거\nIoU와 Bounding Box 평가\nSmall Object Detection\nMulti-scale Training\nObject Detection 데이터 증강\nYOLO 학습 결과 해석\nDetection 오탐 분석\nOpen-vocabulary Detection\nTransformer 기반 Detection\nDETR 계열",{"title":14,"searchDepth":15,"depth":15,"links":16},"",2,[],[18,19],"Deep_Learning","Computer_Vision","2026-08-06 00:00:00 +0900","객체 탐지의 기본 구조와 R-CNN, YOLO, DETR 계열 모델 및 평가 방법을 정리한다.","md",null,"\u002Fdeep_learning\u002Fcomputer_vision\u002F2026\u002F08\u002F06\u002Fobject-detection\u002F",{"layout":26},"post",true,"\u002Fposts\u002Fdeep_learning\u002Fcomputer_vision\u002F2026-08-06-object-detection",{"title":5,"description":21},"posts\u002FDeep_Learning\u002FComputer_Vision\u002F2026-08-06-object-detection","객체 탐지의 주요 모델, 학습 방법, 평가와 오탐 분석 개념을 정리한다.",[33,34,35],"Deep Learning","Computer Vision","Object Detection","kAiuHncmRRaDGQQRj_hrTZqWVSUFC7NyIHXLoj_RPvI",1788744788737]